Fast Fourier Transforms (FFTs) are widely used across scientific computing, from molecular dynamics and signal processing to computational fluid dynamics (CFD),...

Fast Fourier Transforms (FFTs) are widely used across scientific computing, from molecular dynamics and signal processing to computational fluid dynamics (CFD), wireless multimedia, and machine-learning applications. As computational problem sizes scale to increasingly large domains, researchers require the capability to distribute FFT computations across hundreds or thousands of GPUs spanning…

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AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter...

AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter models, and post-training—which can include fine-tuning, reinforcement learning, and other techniques—helps to further increase accuracy for specific tasks, as well as provide models with new capabilities like the ability to reason.

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